现今的高级健康检查中，成像检查占据了很大的比例。计算机断层扫描（CT扫描）能够检测全身，利用X射线穿透人体获得图像。其呈现为由灰度组成的高分辨率黑白图像。预计能够通过基于图像识别技术的人工智能深度学习来帮助医生作出判断。它使用CT图像来识别膀胱和病变，并在图像中进行分割。这些图像可以在不使用开发人员的情况下实现高精度。在本研究中，使用在医学领域中常用的U-Net神经网络扩展编码器的位置，结合ResNet中的ResBlock和DenseNet中的Dense Block进行训练，维护训练参数的同时降低整体识别操作时间。解码器可以与Attention Gates相结合，抑制图像中的无关区域，同时重视重要特征。结合以上算法，我们提出了一种残差稠密注意力（RDA）U-Net模型，用于从腹部扫描的CT图像中识别器官和病变。使用该模型对膀胱及其病变的准确率分别为96％和93％。它们的交并比分别为0.9505和0.8024。平均海斯多夫距离（AVGDIST）分别为0.02和0.12，与其他卷积神经网络相比，总体训练时间缩短了高达44％。

In today's high-order health examination, imaging examination accounts for a large proportion. Computed tomography (CT), which can detect the whole body, uses X-rays to penetrate the human body to obtain images. Its presentation is a high-resolution black-and-white image composed of gray scales. It is expected to assist doctors in making judgments through deep learning based on the image recognition technology of artificial intelligence. It used CT images to identify the bladder and lesions and then segmented them in the images. The images can achieve high accuracy without using a developer. In this study, the U-Net neural network, commonly used in the medical field, was used to extend the encoder position in combination with the ResBlock in ResNet and the Dense Block in DenseNet, so that the training could maintain the training parameters while reducing the overall identification operation time. The decoder could be used in combination with Attention Gates to suppress the irrelevant areas of the image while paying attention to significant features. Combined with the above algorithm, we proposed a Residual-Dense Attention (RDA) U-Net model, which was used to identify organs and lesions from CT images of abdominal scans. The accuracy (ACC) of using this model for the bladder and its lesions was 96% and 93%, respectively. The values of Intersection over Union (IoU) were 0.9505 and 0.8024, respectively. Average Hausdorff distance (AVGDIST) was as low as 0.02 and 0.12, respectively, and the overall training time was reduced by up to 44% compared with other convolution neural networks.